ELF: Embedded Language Flows (Unofficial PyTorch Reproduction)

May 14, 2026 · View on GitHub

Caution

The OpenWebText results are not directly comparable with baselines (MDLM, Duo, FLM, ...) due to tokenization and preprocessing differences used in the ELF paper.

Specifically, ELF uses a custom preprocessed OpenWebText dataset (see openwebtext-t5). This is tokenized with the T5 tokenizer, not the GPT-2 tokenizer which is used in the standard setting in the literature. In addition, the paper's preprocessing pipeline includes a custom packing scheme with full details not disclosed in the paper.

This is an unofficial PyTorch reproduction (OpenWebText Only) of ELF: Embedded Language Flows.
The official JAX/TPU implementation is at https://github.com/lillian039/ELF, and the official checkpoints are in HuggingFace at embedded-language-flows.

This repository was developed using Claude Code.

Reproduction status

OpenWebText (unconditional), ELF-B (105M), 32-step SDE, γ=1.5, SC-CFG=3:

Metric	Paper (TPU v5p-64)	Reproduction (8× B200 DDP, Lightning)
Gen. PPL ↓	24.1	25.61
Entropy	5.15	5.20

Per-epoch results (32-step SDE, 256 samples):

Epoch	Step	Gen. PPL	Entropy
1	38 034	2.73	0.70
2	76 068	37.11	5.17
3	114 102	28.63	5.21
4	152 136	25.00	5.16
5	190 170	25.58	5.19
6	228 204	26.11	5.21

All samples used for the measurements can be found in reproduction/elf_b-owt/eval1000/metrics.jsonl and reproduction/elf_b-owt/per_epoch/metrics.jsonl.

TODO

Train ELF and/or some of the baselines (MDLM, Duo, FLM, ...) in a directly comparable setting (https://huggingface.co/datasets/Skylion007/openwebtext).

What's in this repo

pytorch_lightning/: model, training script (train_lightning.py), eval (eval_lightning.py), and utilities. 8-GPU CUDA DDP via PyTorch Lightning.
reproduction/elf_b-owt/: config snapshot, 1000 final samples, and per-epoch samples. The checkpoint is hosted separately (see Quickstart).

Quickstart — evaluate the reproduced checkpoint

# 1. Environment (conda)
conda env create -f environment.yml -n elf-pytorch && conda activate elf-pytorch

# 2. Download the reproduced final EMA checkpoint (1.4 GB)
pip install huggingface_hub
huggingface-cli download Ugness/elf-torch last.ckpt \
    --local-dir reproduction/elf_b-owt/

# 3. Run the 1000-sample evaluation
cd pytorch_lightning/
torchrun --nproc_per_node=8 --master_port=29510 eval_lightning.py \
    --config configs/training_configs/train_owt_ELF-B.yml \
    --checkpoint_path ../reproduction/elf_b-owt/last.ckpt \
    --num_samples 1000
# Expected: Gen. PPL ≈ 25.6, sample entropy ≈ 5.20.

Per-epoch checkpoints

The checkpoints are under this HF repo: checkpoints/.

# Example: pull epoch 4 ckpt.
huggingface-cli download Ugness/elf-torch \
    checkpoints/checkpoint_epoch03_step00152136.ckpt \
    --local-dir reproduction/elf_b-owt/

Quickstart — train from scratch

cd pytorch_lightning/
torchrun --nproc_per_node=8 --master_port=29501 train_lightning.py \
    --config configs/training_configs/train_owt_ELF-B.yml

Reproduction details

Hardware: 8× NVIDIA B200 (sm_100), CUDA 12.8. broadcast_buffers=False. See pytorch_lightning/train_lightning.py.
Wall-clock: ~3 hours per epoch.

Differences vs the paper run

Aspect	Paper	This reproduction
Hardware	TPU v5p-64	8× B200 DDP
Framework	JAX/Flax	PyTorch Lightning
Epochs	5	6 (one extra to reach entropy ≈ 5.20)
Optimizer / objective	Muon + L2 denoise + CE decode (decoder_prob=0.2)	Unchanged
Schedule, noise scale, time schedule, SC, CFG	Unchanged	Unchanged